explain why I chose smoking data and maybe where my data comes from?? This data package contains the data that powers the chart “Share of adults who smoke” on the Our World in Data website https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators
How does the prevalence of smoking in adults across the world vary in the dataset, and what trends or patterns emerge from this analysis?
The PSY6422_smoke repository is organized into key sections to help you navigate its contents. The /codebook folder provides detailed documentation on the dataset, including variable descriptions and structure, offering essential context for the analysis. The /data folder contains the raw datasets used in this project, forming the basis of all analyses. The /figures folder showcases visualizations and plots created during the analysis, highlighting the project’s key findings and insights. Lastly, the /scripts folder includes all the code used for data processing, analysis, and visualization. Together, these sections guide you through the project workflow, from raw data to final outputs.
The raw dataset for this visualization project comes from : Multiple sources compiled by World Bank (2024) – processed by Our World in Data. “Prevalence of current tobacco use (% of adults)” [dataset]. World Health Organization (via World Bank), “World Development Indicators” [original data]. Source: Multiple sources compiled by World Bank (2024) – processed by Our World In Data
The percentage of the population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”. The rates are age-standardized to the WHO Standard Population.
These considerations are important when interpreting the project’s results Estimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution.
# List of packages to install and load
packages <- c("tidyverse", "ggplot2", "tidyr", "dplyr", "plotly", "rnaturalearth", "rnaturalearthdata", "sf")
# Function to install packages and load them
install_and_load <- function(packages) {
for (package in packages) {
if (!require(package, character.only = TRUE)) {
install.packages(package, dependencies = TRUE)
library(package, character.only = TRUE)
} else {
library(package, character.only = TRUE)
}
}
}
# Run the function
install_and_load(packages)## Loading required package: tidyverse## Warning: package 'ggplot2' was built under R version 4.4.2## Warning: package 'tidyr' was built under R version 4.4.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Loading required package: plotly## Warning: package 'plotly' was built under R version 4.4.2##
## Attaching package: 'plotly'
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## last_plot
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## filter
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## layout
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## Loading required package: rnaturalearth## Warning: package 'rnaturalearth' was built under R version 4.4.2## Loading required package: rnaturalearthdata## Warning: package 'rnaturalearthdata' was built under R version 4.4.2##
## Attaching package: 'rnaturalearthdata'
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## countries110
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## Loading required package: sf## Warning: package 'sf' was built under R version 4.4.2## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE
# Load data
``` r
# Load raw data
rawdata <- read.csv("data/smoking.csv")In order to create my visualization, I had to create new variables. I got variable world from the function sf, which contains the resources necessary for my analysis. I then merged my world data and my smoking data into ‘map_data’ via the ISO code.
after my initial sanity check, I started to clean my data. The data contained specific entities such as different regions of the globe and the different income levels. In order to visualize the data I had to take these out. Further more, I wanted to visualize the data by 5 years so i got rid of 2018 and 2019. I also renamed the variable prevalence for ease.and fixed my missing isos
# Clean data: Remove specific entities
countries_data <- rawdata[!rawdata$Entity %in% c("East Asia and Pacific (WB)", "Sub-Saharan Africa (WB)",
"Upper-middle-income countries", "Europe and Central Asia (WB)",
"World", "European Union (27)", "Low-income countries",
"Lower-middle-income countries", "Middle East and North Africa (WB)",
"Middle-income countries", "North America (WB)", "South Asia (WB)",
"Latin America and Caribbean (WB)", "High-income countries"), ]
# Further clean data: Exclude years 2018 and 2019
countries_data <- countries_data %>%
filter(!(Year %in% c(2018, 2019)))
# Rename the column
countries_data <- countries_data %>%
rename(Prevalence = Prevalence.of.current.tobacco.use....of.adults.) #sanity check
Entity refers to the name of the country. Code refers to the OWID internal entity code that we use if the entity is a country or region. Year refers to the years of the prevalence. Prevalence.of.current.tobacco.use….of.adults. refers to the prevalence of current tobacco users.
subset_data <- plot_data %>% filter(Year %in% c(2000, 2005))
subset_data2 <- plot_data %>% filter(Year %in% c(2010, 2015, 2020))
combined_data <- bind_rows(subset_data, subset_data2)
# Plot using combined data
plot <- plot_ly(
data = combined_data,
type = "choropleth",
locations = ~iso_a3,
locationmode = "ISO-3",
z = ~Prevalence,
frame = ~Year,
text = ~paste("Country:", Entity, "<br>Prevalence:", Prevalence, "%"),
colorscale = "Reds",
zmin = 0,
zmax = 68.5,
showscale = TRUE
) %>%
layout(
title = "Global Smoking Prevalence (Subset Test)",
geo = list(
projection = list(type = "mercator"),
showcoastlines = TRUE,
coastlinecolor = "grey"
)
)
plotplot <- plot_ly(
data = combined_data,
type = "choropleth",
locations = ~iso_a3,
locationmode = "ISO-3",
z = ~Prevalence,
frame = ~Year,
text = ~paste(
"Country:", Entity,
ifelse(is.na(Prevalence), "<br>No Data", paste0("<br>Prevalence: ", Prevalence, "%"))
),
colorscale = list(
c(0, "#ffeda0"), # Low prevalence: light orange
c(0.5, "#feb24c"), # Medium prevalence: orange
c(1, "#67000d") # High prevalence: dark red
),
zmin = 0,
zmax = 68.5,
showscale = TRUE,
marker = list(line = list(color = "grey", width = 0.5)) # Border for the countries
) %>%
layout(
title = "Global Smoking Prevalence (Subset Test)",
geo = list(
projection = list(type = "equirectangular"), # Rectangular projection
showcoastlines = TRUE, # Show coastlines
coastlinecolor = "grey", # Set coastline border color
showcountries = TRUE, # Ensure country borders are shown
countrycolor = "grey", # Set country border color
showland = TRUE, # Show land explicitly
landcolor = "white", # Set land colour to white
showocean = TRUE, # Enable ocean rendering
oceancolor = "lightblue", # Set ocean colour to light blue
showframe = FALSE # Optionally remove frame border
),
annotations = list(
list(
x = 0.5, # Position for the note (to the right of the map)
y = -0.1, # Vertical position (lower part of the map)
xref = "paper", # Reference the x-axis relative to the paper
yref = "paper", # Reference the y-axis relative to the paper
text = "Note: White regions indicate missing data.", # Your note
showarrow = FALSE, # Disable arrow pointing
font = list(size = 12, color = "black"), # Font size and color
align = "left"
)
)
)
# Display the plot
plot##final final plot
plot <- plot_ly(
data = combined_data,
type = "choropleth",
locations = ~iso_a3,
locationmode = "ISO-3",
z = ~Prevalence,
frame = ~Year,
text = ~paste(
"Country:", Entity,
ifelse(is.na(Prevalence), "<br>No Data", paste0("<br>Prevalence: ", Prevalence, "%"))
),
colorscale = list(
c(0, "#ffeda0"), # Low prevalence: light orange
c(0.5, "#feb24c"), # Medium prevalence: orange
c(1, "#67000d") # High prevalence: dark red
),
zmin = 0,
zmax = 68.5,
showscale = TRUE,
marker = list(line = list(color = "grey", width = 0.5)) # Border for the countries
) %>%
layout(
title = "Global Smoking Prevalence (Subset Test)",
geo = list(
projection = list(type = "equirectangular"), # Rectangular projection
showcoastlines = TRUE, # Show coastlines
coastlinecolor = "grey", # Set coastline border color
showcountries = TRUE, # Ensure country borders are shown
countrycolor = "grey", # Set country border color
showland = TRUE, # Show land explicitly
landcolor = "white", # Set land colour to white
showocean = TRUE, # Enable ocean rendering
oceancolor = "lightblue", # Set ocean colour to light blue
showframe = FALSE # Optionally remove frame border
),
annotations = list(
list(
x = 0.5, # Position for the note (to the right of the map)
y = -0.1, # Vertical position (lower part of the map)
xref = "paper", # Reference the x-axis relative to the paper
yref = "paper", # Reference the y-axis relative to the paper
text = "Note: White regions indicate missing data.", # Your note
showarrow = FALSE, # Disable arrow pointing
font = list(size = 12, color = "black"), # Font size and color
align = "left"
)
)
)
# Display the plot
plot